Name
Abstract | ||
|---|---|---|
Video anomaly detection is an essential task in computer vision which attracts massive attention from academia and industry. The existing approaches are implemented in diverse deep learning frameworks and settings, making it difficult to reproduce the results published by the original authors. Undoubtedly, this phenomenon is detrimental to the development of Video Anomaly detection and community communication. In this paper, we present a PyTorch-based video anomaly detection toolbox, namely PyAnomaly that contains high modular and extensible components, comprehensive and impartial evaluation platforms, a friendly manageable system configuration, and the abundant engineering deployment functions. To make it easy-to-use and easy-to-extend, we implement the architecture by hooks and registers functionality. Remarkably, we have reproduced the comparable experimental results of six representative methods as those published by the original authors, and we will release these pre-trained models with more rich configurations. To our best knowledge, the PyAnomaly is the first open-source tool in video anomaly detection and is available at https://github.com/YuhaoCheng/PyAnomaly.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3394171.3414540 | MM '20: The 28th ACM International Conference on Multimedia
Seattle
WA
USA
October, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7988-5 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuhao Cheng | 1 | 12 | 2.22 |
| Wu Liu | 2 | 275 | 34.53 |
| Peng-Rui Duan | 3 | 13 | 2.99 |
| Jingen Liu | 4 | 807 | 34.41 |
| Tao Mei | 5 | 4702 | 288.54 |